Hematology

Lymphoma

Latest AI and machine learning research in lymphoma for healthcare professionals.

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Deep learning-based identification of acute ischemic core and deficit from non-contrast CT and CTA.

The accurate identification of irreversible infarction and salvageable tissue is important in planni...

Non-line-of-Sight Imaging via Neural Transient Fields.

We present a neural modeling framework for non-line-of-sight (NLOS) imaging. Previous solutions have...

Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG.

Currently there are no reliable means of identifying infants at-risk for later language disorders. I...

Deep Learning-based Recalibration of the CUETO and EORTC Prediction Tools for Recurrence and Progression of Non-muscle-invasive Bladder Cancer.

Despite being standard tools for decision-making, the European Organisation for Research and Treatme...

Hybridized neural networks for non-invasive and continuous mortality risk assessment in neonates.

Premature birth is the primary risk factor in neonatal deaths, with the majority of extremely premat...

ILDMSF: Inferring Associations Between Long Non-Coding RNA and Disease Based on Multi-Similarity Fusion.

The dysregulation and mutation of long non-coding RNAs (lncRNAs) have been proved to result in a var...

A semi-supervised deep learning approach for predicting the functional effects of genomic non-coding variations.

BACKGROUND: Understanding the functional effects of non-coding variants is important as they are oft...

k-Space-based coil combination via geometric deep learning for reconstruction of non-Cartesian MRSI data.

PURPOSE: State-of-the-art whole-brain MRSI with spatial-spectral encoding and multichannel acquisiti...

Framed and non-framed robotics in neurosurgery: A 10-year single-center experience.

BACKGROUND: Safety, efficacy and efficiency of neurosurgical robots are defined by their design (i.e...

Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches.

Biological pest control (i.e. 'biocontrol') agents can have direct and indirect non-target impacts, ...

Immunomodulatory and antimicrobial non-mulberry silk fibroin accelerates fibroblast repair and regeneration by protecting oxidative stress.

The antimicrobial nature of silk-fibroin (SF) is reported but antioxidant potential and the immunom...

Spectral embedding network for attributed graph clustering.

Attributed graph clustering aims to discover node groups by utilizing both graph structure and node ...

Automatic detect lung node with deep learning in segmentation and imbalance data labeling.

In this study, a novel method with the U-Net-based network architecture, 2D U-Net, is employed to se...

Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common tumor entity spreading to the brai...

A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics.

Recent developments of single-cell RNA-seq (scRNA-seq) technologies have led to enormous biological ...

Role of Regulatory Non-Coding RNAs in Aggressive Thyroid Cancer: Prospective Applications of Neural Network Analysis.

Thyroid cancer (TC) is the most common endocrine malignancy. Most TCs have a favorable prognosis, wh...

Comparative antioxidant potential of kefir and yogurt of bovine and non-bovine origins.

UNLABELLED: The aim of this study was to compare the antioxidant potential of the yogurt and kefir p...

Non-Contact Respiration Measurement Method Based on RGB Camera Using 1D Convolutional Neural Networks.

Conventional respiration measurement requires a separate device and/or can cause discomfort, so it i...

Neural network aided approximation and parameter inference of non-Markovian models of gene expression.

Non-Markovian models of stochastic biochemical kinetics often incorporate explicit time delays to ef...

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